Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be q... more Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite useful in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on severity of the infection. First, we pre-process the X-ray images using median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 model and SVM classifier are then used respectively for feature extraction and classification. The result of the classification model confirms that compared with the alternatives, with chest X-Ray images, the ResNet-50 model produced remarkable classification results in terms of accuracy (95%), recall (0.94), F1-Score (0.92), and precision (0.91).
Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be q... more Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite useful in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on severity of the infection. First, we pre-process the X-ray images using median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 model and SVM classifier are then used respectively for feature extraction and classification. The result of the classification model confirms that compared with the alternatives, with chest X-Ray images, the ResNet-50 model produced remarkable classification results in terms of accuracy (95%), recall (0.94), F1-Score (0.92), and precision (0.91).
Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be q... more Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite useful in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on severity of the infection. First, we pre-process the X-ray images using median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 model and SVM classifier are then used respectively for feature extraction and classification. The result of the classification model confirms that compared with the alternatives, with chest X-Ray images, the ResNet-50 model produced remarkable classification results in terms of accuracy (95%), recall (0.94), F1-Score (0.92), and precision (0.91).
Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be q... more Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite useful in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on severity of the infection. First, we pre-process the X-ray images using median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 model and SVM classifier are then used respectively for feature extraction and classification. The result of the classification model confirms that compared with the alternatives, with chest X-Ray images, the ResNet-50 model produced remarkable classification results in terms of accuracy (95%), recall (0.94), F1-Score (0.92), and precision (0.91).
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